In this position paper, we explore standard language ideology in language
generated by large language models (LLMs). First, we outline how standard
language ideology is reflected and reinforced in LLMs. We then present a
taxonomy of open problems regarding standard language ideology in AI-generated
language with implications for minoritized language communities. We introduce
the concept of standard AI-generated language ideology, the process by which
AI-generated language regards Standard American English (SAE) as a linguistic
default and reinforces a linguistic bias that SAE is the most "appropriate"
language. Finally, we discuss tensions that remain, including reflecting on
what desirable system behavior looks like, as well as advantages and drawbacks
of generative AI tools imitating--or often not--different English language
varieties. Throughout, we discuss standard language ideology as a manifestation
of existing global power structures in and through AI-generated language before
ending with questions to move towards alternative, more emancipatory digital
futures.

研究中探讨了大型语言模型（LLMs）生成的语言中标准语言意识形态的反映和加强，提出了与 AI 生成语言中标准语言意识形态相关的问题，并介绍了标准 AI 生成语言意识形态的概念。同时，讨论了仍然存在的问题和生成 AI 工具模仿不同英语语言变体的优缺点，将标准语言意识形态视为现有全球权力结构在 AI 生成语言中的表现，并呼吁探索替代性、更解放性的数字未来。

人工智能生成语言中的标准语言意识形态

Standard Language Ideology in AI-Generated Language

Large Language Models (LLMs) have shown impressive performance across a
variety of Artificial Intelligence (AI) and natural language processing tasks,
such as content creation, report generation, etc. However, unregulated malign
application of these models can create undesirable consequences such as
generation of fake news, plagiarism, etc. As a result, accurate detection of
AI-generated language can be crucial in responsible usage of LLMs. In this
work, we explore 1) whether a certain body of text is AI generated or written
by human, and 2) attribution of a specific language model in generating a body
of text. Texts in both English and Spanish are considered. The datasets used in
this study are provided as part of the Automated Text Identification
(AuTexTification) shared task. For each of the research objectives stated
above, we propose an ensemble neural model that generates probabilities from
different pre-trained LLMs which are used as features to a Traditional Machine
Learning (TML) classifier following it. For the first task of distinguishing
between AI and human generated text, our model ranked in fifth and thirteenth
place (with macro $F1$ scores of 0.733 and 0.649) for English and Spanish
texts, respectively. For the second task on model attribution, our model ranked
in first place with macro $F1$ scores of 0.625 and 0.653 for English and
Spanish texts, respectively.

本文提出了一种集成神经模型以检测大型语言模型生成的语言，并对模型进行归因的方法。

基于集成 LLM 方法的生成式人工智能文本分类

Generative AI Text Classification using Ensemble LLM Approaches

Human communication is increasingly intermixed with language generated by AI.
Across chat, email, and social media, AI systems suggest words, complete
sentences, or produce entire conversations. AI-generated language is often not
identified as such but presented as language written by humans, raising
concerns about novel forms of deception and manipulation. Here, we study how
humans discern whether verbal self-presentations, one of the most personal and
consequential forms of language, were generated by AI. In six experiments,
participants (N = 4,600) were unable to detect self-presentations generated by
state-of-the-art AI language models in professional, hospitality, and dating
contexts. A computational analysis of language features shows that human
judgments of AI-generated language are hindered by intuitive but flawed
heuristics such as associating first-person pronouns, use of contractions, or
family topics with human-written language. We experimentally demonstrate that
these heuristics make human judgment of AI-generated language predictable and
manipulable, allowing AI systems to produce text perceived as "more human than
human." We discuss solutions, such as AI accents, to reduce the deceptive
potential of language generated by AI, limiting the subversion of human
intuition.

人类对 AI 产生的虚假言论的辨别能力不足，该文讨论了造成这一现象的原因，并提出了解决方案。